{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 快速查看整体信息\n",
    "上一章讲到了控制DataFrame显示的一些参数，本章则具体讲解一下如何获得对DataFrame的整体认知。\n",
    "\n",
    "__auther__ = 'zhenhang.sun@gmail.com'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\新建文件夹\\\\pandas-tutorial'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. `.info()`\n",
    "这是DataFrame才可用的API，快捷查看多种信息：总行数和列数、每列元素类型和non-NaN的个数，总内存。\n",
    "#### `DataFrame.info(verbose=None, memory_usage=True, null_counts=True)`\n",
    "- verbose：True or False，字面意思是冗长的，也就说如何DataFrame有很多列，是否显示所有列的信息，如果为否，那么会省略一部分；\n",
    "- memory_usage：True or False，默认为True，是否查看DataFrame的内存使用情况；\n",
    "- null_counts：True or False，默认为True，是否统计NaN值的个数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 100 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame( columns = range(0,100))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 0 entries\n",
      "Data columns (total 100 columns):\n",
      "0     0 non-null object\n",
      "1     0 non-null object\n",
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      "4     0 non-null object\n",
      "5     0 non-null object\n",
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      "32    0 non-null object\n",
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      "34    0 non-null object\n",
      "35    0 non-null object\n",
      "36    0 non-null object\n",
      "37    0 non-null object\n",
      "38    0 non-null object\n",
      "39    0 non-null object\n",
      "40    0 non-null object\n",
      "41    0 non-null object\n",
      "42    0 non-null object\n",
      "43    0 non-null object\n",
      "44    0 non-null object\n",
      "45    0 non-null object\n",
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      "48    0 non-null object\n",
      "49    0 non-null object\n",
      "50    0 non-null object\n",
      "51    0 non-null object\n",
      "52    0 non-null object\n",
      "53    0 non-null object\n",
      "54    0 non-null object\n",
      "55    0 non-null object\n",
      "56    0 non-null object\n",
      "57    0 non-null object\n",
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      "60    0 non-null object\n",
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      "77    0 non-null object\n",
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      "90    0 non-null object\n",
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      "92    0 non-null object\n",
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      "95    0 non-null object\n",
      "96    0 non-null object\n",
      "97    0 non-null object\n",
      "98    0 non-null object\n",
      "99    0 non-null object\n",
      "dtypes: object(100)\n",
      "memory usage: 0.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()  # 直接默认设置即可"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# 2. `.ndim, .shape, .size`\n",
    "查看维数，形状，元素个数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B\n",
       "0  NaN  2.0\n",
       "1  3.0  NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame( [[np.nan, 2],[3,np.nan]], columns = ['A','B'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim  # 返回维度数，Series一维，DataFrame两维，平时很少用到，不过有时会在循环中用到"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape  # (行数，列数)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size   # 元素个数，rows×cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# 3. `.head(), .tail()`\n",
    "默认分别查看头5行和后5行。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `Series/DataFrame.head(n=5)`\n",
    "#### `Series/DataFrame.tail(n=5)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "3    3\n",
       "4    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series( range(0,5))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "dtype: int32"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    2\n",
       "3    3\n",
       "4    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# 4. `.memory_usage()`\n",
    "比info中内存显示更可控一些，单位是**字节**。\n",
    "#### `Series/DataFrame.memory_usage(index=True, deep=False)`\n",
    "- index：是否显示索引占用的内存，毫无疑问索引也占用内存；\n",
    "- deep：是否显示object类型的列消耗的系统资源，由于pandas中object元素只是一个引用，我估计这个deep是指显示真实的内存占用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index      80\n",
       "numeric    24\n",
       "object     24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.memory_usage(deep = False)  # Index即索引占用内存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index       80\n",
       "numeric     24\n",
       "object     186\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.memory_usage(deep = True)  # object 型占用的内存变大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# 5. `.describe()`\n",
    "快速查看每一列的统计信息，默认排除所有NaN元素。\n",
    "#### `DataFrame.describe( include= [np.number])`\n",
    "- include：'all'或者[np.number 或 np.object]。numberic只对元素属性为数值的列做数值统计，object只对元素属性为object的列做类字符串统计。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numeric</th>\n",
       "      <th>object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   numeric object\n",
       "0        1      a\n",
       "1        2      b\n",
       "2        1      b"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame( [[1,'a'],[2,'b'],[1,'b']], columns = ['numeric','object'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numeric     int64\n",
       "object     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "      <th></th>\n",
       "      <th>numeric</th>\n",
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       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.577350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        numeric\n",
       "count  3.000000\n",
       "mean   1.333333\n",
       "std    0.577350\n",
       "min    1.000000\n",
       "25%    1.000000\n",
       "50%    1.000000\n",
       "75%    1.500000\n",
       "max    2.000000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()  # 默认只对数值列进行统计 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       object\n",
       "count       3\n",
       "unique      2\n",
       "top         b\n",
       "freq        2"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe( include=[np.object]) # 只对object型列进行统计，类别统计方式，只统计这四种"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
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   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>std</th>\n",
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       "      <th>50%</th>\n",
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      "text/plain": [
       "         numeric object\n",
       "count   3.000000      3\n",
       "unique       NaN      2\n",
       "top          NaN      b\n",
       "freq         NaN      2\n",
       "mean    1.333333    NaN\n",
       "std     0.577350    NaN\n",
       "min     1.000000    NaN\n",
       "25%     1.000000    NaN\n",
       "50%     1.000000    NaN\n",
       "75%     1.500000    NaN\n",
       "max     2.000000    NaN"
      ]
     },
     "execution_count": 57,
     "metadata": {},
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   ],
   "source": [
    "df.describe( include = 'all')  # 数值序列和object序列共同统计的信息只有count: non-NaN元素个数"
   ]
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